Periodic Reporting for period 1 - SnowMagnet (Low capillary number flow in phase change porous media: permeability and liquid water capacity of snow.)
Berichtszeitraum: 2022-11-01 bis 2024-10-31
The movement of water through snow is a key factor in seasonal flooding and glacier hydrology, yet its behavior varies significantly due to the complex microstructure of snow. This variability spans several orders of magnitude and is poorly understood, particularly in unsaturated flow conditions. Existing models struggle to accurately represent water transport in snow because they lack a fundamental understanding of pore-scale mechanisms, including liquid water displacement, diffusion, and phase transitions.
The SnowMagnet project aims to address these gaps by pioneering nuclear magnetic resonance (NMR) imaging studies on wet snow at the pore scale, a method that overcomes the limitations of micro-computed tomography for transient flow conditions. By combining NMR with Lattice-Boltzmann simulations and Pore-Network models, the project will provide quantitative insights into local saturation dynamics, capillary-driven flow, and hydraulic conductivity across different snow microstructures.
A key experimental approach involves 3D-printed porous media, including precisely replicated snow geometries, to refine measurement techniques and validate models before applying them to real snow samples. These controlled experiments, coupled with simulations, will generate high-resolution datasets on unsaturated flow as a function of the capillary number. This data will be instrumental in calibrating dynamic pore-network models, enabling the development of improved constitutive laws and a new parameterization of effective hydraulic conductivity for snow.
By resolving water transport mechanisms at the pore scale and linking them to macroscopic flow behavior, the project will establish a new standard for snow hydrology models. This advancement will enhance predictions of snowmelt behavior, contributing to better flood forecasting, glacier mass balance assessments, and hydrological modeling in cold-region environments.
The SnowMagnet project aims to address these gaps by pioneering nuclear magnetic resonance (NMR) imaging studies on wet snow at the pore scale, a method that overcomes the limitations of micro-computed tomography for transient flow conditions. By combining NMR with Lattice-Boltzmann simulations and Pore-Network models, the project will provide quantitative insights into local saturation dynamics, capillary-driven flow, and hydraulic conductivity across different snow microstructures.
A key experimental approach involves 3D-printed porous media, including precisely replicated snow geometries, to refine measurement techniques and validate models before applying them to real snow samples. These controlled experiments, coupled with simulations, will generate high-resolution datasets on unsaturated flow as a function of the capillary number. This data will be instrumental in calibrating dynamic pore-network models, enabling the development of improved constitutive laws and a new parameterization of effective hydraulic conductivity for snow.
By resolving water transport mechanisms at the pore scale and linking them to macroscopic flow behavior, the project will establish a new standard for snow hydrology models. This advancement will enhance predictions of snowmelt behavior, contributing to better flood forecasting, glacier mass balance assessments, and hydrological modeling in cold-region environments.
This project investigated water movement in porous materials and snow using magnetic resonance imaging (MRI). The study was divided into two main sections: experiments on model porous media, including 3D-printed snow samples, and experiments on natural snow under melting and infiltration conditions.
MRI techniques were used to capture spatial and temporal dynamics of water movement in porous materials. While high-resolution 3D imaging was used to observe the before and after states of drainage, faster one-dimensional (1D) vertical profiling was employed to track rapid localized fluid flow fluctuations. These so-called Haines jumps occur in less than 0.1 seconds and were captured using imaging speeds down to 17 milliseconds per frame. Additionally, the phase-angle data and second-echo imaging provided insights into higher-order fluid flow properties such as turbulence and acceleration.
To better understand flow behavior in snow, 3D-printed porous structures based on CT scans of real snow samples were tested. These models allowed controlled studies of water movement in porous media that resemble natural snow at the microstructural level. Although differences in scale, wettability, and phase transitions meant they did not fully replicate natural snow conditions, they revealed a non-trivial increase in signal heterogeneity.
Experiments on natural snow posed significant challenges due to temperature control constraints and limited space for meltwater collection. The focus of these experiments was therefore on snow melting, meltwater accumulation, and interactions with soil-like porous media. The rapid profile images were interleaved with 2D slices to track meltwater distribution and calibration of liquid water content. An example of such an experiment is included in Figure 2, where we observed meltwater accumulating at the interface between snow and soil, where direct percolation was prevented by a capillary barrier. The local pressure head increased with continued melting, eventually overcoming the entry pressure, at which point percolation into the underlying material began. The snow gradually compacted, preserving layered liquid water distributions, suggesting slow restructuring of the ice matrix.
In rain-on-snow experiments, a key comparison was made between water infiltration in snow and a bead-pack sample. While water infiltrated gradually and evenly in the bead pack, snow exhibited discontinuous and heterogeneous liquid water distribution from the start. This suggests that melting ice has unique wetting properties, which facilitate rapid water transport into smaller pores at a much larger length scale than soil-like porous media. This distinction indicates that the hydraulic behavior of snow is fundamentally different from other porous materials.
These findings provide valuable new insights into snowmelt and water infiltration dynamics, which can help refine hydrological models and improve predictions of snowmelt and runoff behavior.
MRI techniques were used to capture spatial and temporal dynamics of water movement in porous materials. While high-resolution 3D imaging was used to observe the before and after states of drainage, faster one-dimensional (1D) vertical profiling was employed to track rapid localized fluid flow fluctuations. These so-called Haines jumps occur in less than 0.1 seconds and were captured using imaging speeds down to 17 milliseconds per frame. Additionally, the phase-angle data and second-echo imaging provided insights into higher-order fluid flow properties such as turbulence and acceleration.
To better understand flow behavior in snow, 3D-printed porous structures based on CT scans of real snow samples were tested. These models allowed controlled studies of water movement in porous media that resemble natural snow at the microstructural level. Although differences in scale, wettability, and phase transitions meant they did not fully replicate natural snow conditions, they revealed a non-trivial increase in signal heterogeneity.
Experiments on natural snow posed significant challenges due to temperature control constraints and limited space for meltwater collection. The focus of these experiments was therefore on snow melting, meltwater accumulation, and interactions with soil-like porous media. The rapid profile images were interleaved with 2D slices to track meltwater distribution and calibration of liquid water content. An example of such an experiment is included in Figure 2, where we observed meltwater accumulating at the interface between snow and soil, where direct percolation was prevented by a capillary barrier. The local pressure head increased with continued melting, eventually overcoming the entry pressure, at which point percolation into the underlying material began. The snow gradually compacted, preserving layered liquid water distributions, suggesting slow restructuring of the ice matrix.
In rain-on-snow experiments, a key comparison was made between water infiltration in snow and a bead-pack sample. While water infiltrated gradually and evenly in the bead pack, snow exhibited discontinuous and heterogeneous liquid water distribution from the start. This suggests that melting ice has unique wetting properties, which facilitate rapid water transport into smaller pores at a much larger length scale than soil-like porous media. This distinction indicates that the hydraulic behavior of snow is fundamentally different from other porous materials.
These findings provide valuable new insights into snowmelt and water infiltration dynamics, which can help refine hydrological models and improve predictions of snowmelt and runoff behavior.
These rapid-profiling MRI experiments provide unprecedented insights into water movement in porous materials, particularly in snow. The combination of high temporal resolution and 2D cross-sectional imaging allows for the detailed observation of fluid dynamics, capillary effects, and structural changes that were previously difficult to capture. These findings are essential for improving hydraulic models for porous media, particularly in snow hydrology, where they can enhance predictions of snowmelt, water infiltration, and runoff dynamics.
Beyond snow, this method presents exciting opportunities for studying other porous materials with complex flow behaviors, such as sea ice, permafrost, and engineered porous systems. Future research should focus on integrating active temperature control within the spectrometer while maintaining the same sample size. This advancement would enable the study of processes where transient flow dynamics and phase transitions play a critical role.
The insights gained from these experiments will also contribute to the refinement of pore-network models, ensuring they incorporate more complex transport mechanisms. By improving our understanding of water dynamics in porous media, this research has the potential to impact a wide range of scientific and environmental applications.
Beyond snow, this method presents exciting opportunities for studying other porous materials with complex flow behaviors, such as sea ice, permafrost, and engineered porous systems. Future research should focus on integrating active temperature control within the spectrometer while maintaining the same sample size. This advancement would enable the study of processes where transient flow dynamics and phase transitions play a critical role.
The insights gained from these experiments will also contribute to the refinement of pore-network models, ensuring they incorporate more complex transport mechanisms. By improving our understanding of water dynamics in porous media, this research has the potential to impact a wide range of scientific and environmental applications.